The present invention relates to a vigilance monitoring system. In particular the invention relates to a system for monitoring, recording and/or analysing vigilance, alertness or wakefulness and/or a stressed state of an operator of equipment or machinery in a variety of situations including situations wherein the degree of vigilance of the operator has implications for the safety or well being of the operator or other persons. A typical application may include monitoring the driver of a vehicle or pilot of an aircraft, although the invention also has applications in areas involving related occupations such as train drivers and operators of equipment such as cranes and industrial machinery in general, and where lack of operator vigilance can give rise to harmful social or economic consequences.
The system of the present invention will be described herein with reference to monitoring a driver of a vehicle nevertheless it is not thereby limited to such applications. For example, other applications may include monitoring routine, acute or sub-acute physiological parameters of a person or subject in a home, work, clinic or hospital environment. The monitored parameters may include cardiac, respiratory and movement parameters as well as parameters relating to apnea events, subject sleep states or sudden death syndrome on-set.
The monitoring system is designed, inter alia, to provide non-invasive monitoring of a driver""s physiological data including movement activity, heart activity, respiration and other physiological functions. The monitored physiological data may undergo specific analysis processing to assist in determining of the driver""s state of vigilance. The system is designed to detect various states of the driver""s activity and detect certain conditions of driver fatigue or relaxation state that could lead to an unsafe driving condition or conditions.
The system of the present invention includes means for gathering movement data associated with the driver. The movement gathering means may include a plurality of sensors such as touch sensitive mats placed in locations of the vehicle that make contact with the driver, such as the seat, steering wheel, pedal(s), seat belt or the like. Each location may include several sensors or mats to more accurately monitor movements of the driver.
Signals from the various sensors/mats may be processed and analysed by a processing means. The processing means may include a digital computer. The processing means may be programmed to recognize particular movement signatures or patterns of movement, driver posture or profile and to interpret these to indicate that vigilance has deteriorated or is below an acceptable threshold. The processing means may include one or more algorithms.
The sensors or mats may include piezoelectric, electrostatic, piezo ceramic or strain gauge material. The latter may be manufactured by separating two conductive materials such as aluminium foil with an electrolyte material which is capable of passing AC but not DC current. In one form the sensors or mats may include Capacitive Static Discharge (CSD) or Polyvinylidene fluoride (PVDF) material. The sensors/mats may be covered with a non-obtrusive, flexible surface which is capable of detecting pressure and/or monitoring electrophysiological activity.
The pressure detecting capability may be used for detecting driver movement. The or each sensor may produce an output signal that represents the magnitude of the pressure or force that is applied to the sensor. The or each pressure signal may thus represent an absolute or quantitative measure of pressure applied to the sensor. The electrophysiological activity may include electrical signals generated by the body of the driver eg. electrical muscle activity and/or pulse activity.
The sensors or mats may be located in various parts of a vehicle. The seat of the driver may be divided into several sections such as upper or back and lower or seat. The upper or back section may include sensors in the top edge, centre and base. The lower or seat section may include sensors in the front edge, centre and rear. The or each sensor may include CSD or PVDF material,
The steering wheel may include a plurality of sensors. The steering wheel may be divided into eight zones such as upper, upper left, upper right, left, right, lower left, lower right and lower. At least one sensor may be associated with each zone. The or each sensor may include CSD or PVDF material.
The floor covering such as carpet may include a plurality of sensors. The floor covering or carpet may be divided into a plurality of zones. At least one sensor may be associated with each zone. The or each sensor may include CSD or PVDF material.
The accelerator, clutch and brake pedals may include a plurality of sensors. Each pedal may be divided into a plurality of zones such as upper, middle and lower. At least one sensor may be associated with each zone. The or each sensor may include CSD, PVDF or other movement sensitive material.
The seat belt may include one or a plurality of sensors. In one form a sensor or sensors may be embedded in the fixed (i.e. non-retractable) section of the seat belt. The or each sensor may include CSD or PVDF material.
In some embodiments a head tilt device incorporating a positional switch or the like may be associated with the drivers cap, glasses or goggles or may be arranged to clip over the drivers ear or glasses. The head tilt device may be adapted to provide a signal or data which alters in accordance with the position of the driver""s head. Alternatively a radio tracking device may determine and track a subject""s head movements.
In critical applications of vigilance monitoring including applications involving pilots of aircraft, persons responsible for navigating/controlling shipping and drivers of road or rail transport it may be desirable to utilize more comprehensive methods of vigilance monitoring. The latter may include techniques used in conventional sleep monitoring. A head band and/or chin band sensor may be used to monitor EEG, EMG and EOG signals. The head band sensor may include separate left and right frontal zones and left and right eye zones. The sensor may include CSD or PVDF material or other material sensitive to measuring patient skin electrical surface variations and/or impedance.
Various sensors/techniques may be adapted for monitoring eye movement including those based on reflected light, electric skin potential, contact lenses, limbus tracking, video imaging and magnetic induction. The sensors/techniques may include EOG electrodes, infrared detection of eye movements and/or video tracking and processing of eye movements. The sensors/techniques may be adapted for monitoring the left eye only or the right eye only or both eyes.
Raw data which is collected from the various sensors positioned around the vehicle may be filtered and amplified prior to processing and analysis. A significant purpose of the processing and analysis is to determine the driver""s state of vigilance, alertness or wakefulness. In some embodiments, the system may be adapted to effect remedial action, ie. the system may take steps to alert the driver or to actively intervene in the control of the vehicle, when it is deemed that such action is warranted or desirable.
Processing of data may be performed in several stages, including primary, secondary and tertiary analysis.
Primary analysis refers to processing of raw data from the various sensors. This raw data may be filtered and amplified prior to analog to digital conversion. Primary analysis may be adapted to determine valid body movements of the driver as distinct from spurious signals and artefacts due to environmental factors including noise.
Valid body movements may be determined by applying a combination of processing techniques including:
1. signal threshold detection whereby signals below or above a pre-determined threshold are ignored and/or classified as noise or artefact,
2. frequency filtering whereby high-pass, low-pass and notch filters are adapted to remove noise and artefact signals,
3. signal compression whereby data is minimized by presenting main data points such as signal peaks, troughs, averages and zero crossings,
4. half period, amplitude analysis of signals, including analysis as disclosed in AU Patent 632932 entitled xe2x80x9cAnalysis System for Physiological Variablesxe2x80x9d, assigned to the present applicant, the disclosure of which is incorporated herein by cross reference.
Threshold detection may facilitate distinguishing random and non-significant electrical noise (typically spikes of small duration) relative to signals representing valid or actual body movements. Threshold detection may apply to both amplitude and duration of the signals. The relevant threshold(s) may be determined from clinical trials and/or historical data. Where the detection is based on amplitude it may be determined in both negative and positive phases of the signal. Amplitude detection may be based on a measurement of the peak-to-peak signal. Alternatively, the positive and negative peak amplitudes can be measured separately. Threshold detection may be combined with a form of zero-base line detection so that electronic offsets do not adversely affect the accuracy of threshold detections. Each body movement which exceeds the predetermined amplitude and/or duration may be classified as an event for further processing.
Secondary analysis may be adapted to process the results of primary analysis. Secondary analysis may process data for the purpose of presentation and/or display. Data may be displayed or printed in a tabular, graphical or other format which facilitates interpretation of the data. One purpose of the representation and/or display is to represent a driver""s state of vigilance and/or fatigue. In one form each event identified during primary analysis may be counted for a fixed period of time or epoch. The fixed period of time may be 30 seconds or 60 seconds, or other period which is adequate for determining body movement trends. The count value or number of valid body movements in a given period (eg. 30 seconds) may be represented in a display as, say, the length of a vertical bar.
Where it is desired to display the energy or power associated with valid body movements in a particular epoch or time period, the average amplitude associated with each event may be indicated by the length of the vertical bar whilst the count value or number of valid body movements for each epoch may be represented by colouring the vertical bar. For example the colours green, blue, yellow, orange, red may indicate count values or movement numbers in ascending order ie. green indicating the lowest movement number for a particular epoch and red indicating the highest movement number for a particular epoch. Alternatively, data may be displayed on a 3 dimensional graph wherein for example the x dimension of the graph represents time or epochs, the y dimension represents the average amplitude, while the z dimension represents the number of events during a particular epoch. The above display techniques may facilitate interpretation of the number of valid body movements and the amplitude of those movements and association of this data with the driver""s activity or state of vigilance, alertness or wakefulness.
It may also be relevant to measure the period of each individual body movement as this may provide an indication of the energy that is associated with the movement. For example, if a driver squeezes the steering wheel in a rapid response as distinct from gripping the wheel as part of a focussed steering manoeuvre, the pattern of signals in each case will be different. The rapid response may appear as a small cluster of movements/signals or as a single movement/signal with a relatively short duration or period of time. In contrast, the steering manoeuvre may appear as a larger cluster of movements/signals over a relatively longer period of time or as a single movement/signal having a relatively long duration.
The type of signal which may be expected (cluster or single movement/signal) will depend in part upon the type of sensor. For example, piezo ceramic or PVDF sensors may emit fewer clusters of signals but may emit signals with larger time periods in relation to the actual period of the movement which is being monitored. A capacitive electrostatic sensor is more likely to emit clusters of xe2x80x9cspikesxe2x80x9d being relatively short period signals. It may be necessary to record the energy level of each movement as this energy level may fall below a certain threshold when the driver is in a fatigued state. If, for example the driver has relaxed, then the energy of a body movement in the actions of driving may be significantly more subdued than in the case where the driver is alert, and his muscle activity is significantly greater. Therefore it may be useful to measure and record each and every body movement. This data could be displayed on high-resolution graphs where for example the X-axis represents xc2xd second periods and 960 lines make up each continuous section or 480 seconds (8 minutes). The largest amplitude signal in each xc2xd second period could then be displayed on the X-Axis. The Y-Axis on the other hand could be represented by a scale of amplitudes representing each body movement. This graph would be more precise in representing the actual signal level of each body-movement and the subsequent muscle status for a driver.
It may also be useful to detect events that are represented by groups of movements, where, for example, the groups of movements may be indicative of a driver activity of interest. Detection of groups of movements may include user configurable or preset values for;
the maximum time between consecutive body-movements in order to qualify as being counted as part of a periodic body-movement.
the number of consecutive body-movements that are required to qualify for a periodic movement.
the time period during which this number of body-movements must exist in order to qualify as a periodic body-movement.
Periodic measurement analysis can detect, for example, absence of movements which can be associated with a driver""s fatigue.
Tertiary analysis may be adapted to process the results of secondary analysis. One purpose of tertiary analysis is to determine the status of a drivers state of vigilance, alertness or wakefulness. Tertiary analysis may process the results of secondary analysis to produce intermediate data and/or indicate trends in the data. The intermediate data and trends may be used to provide summary reports and further tabular and/or graphic representations of a drivers status or condition. The intermediate data may be processed by one or more vigilance algorithms to determine the status of a driver""s vigilance, alertness or wakefulness. Intermediate data of various types may be derived and the vigilance algorithm(s) may make use of such data to determine the status of the driver. The intermediate data may include:
Rate of change of body movement detections
Rate of change of body movement amplitudes
Area under curve of time versus body movement, for various sequential epochs to detect trends of subject movement changes (amplitude or number of movements)
Correlation of sensor data for patterns of amplitude, energy and body movement changes that can be associated with driver fatigue
Change in frequency of body movement signals
Change in amplitude periods of body movement signals
Change in phase relationships of body movement signals
Relative phase relationship between each section and other types of sensor sections.
Following tertiary analysis the vigilance algorithm(s) may be adapted to correlate the intermediate data and/or apply combinational logic to the data to detect patterns of movement (or lack thereof) which, based on historical data or clinical trials, indicates that the driver is or may be excessively relaxed or is below an acceptable threshold of vigilance, alertness or wakefulness.
The vigilance algorithm(s) may incorporate one or more look up tables including reference movement data and default values associated with acceptable and unacceptable levels of driver fatigue. Histograms including movement histograms of the kind described in AU Patent 632932 based on the work of Rechitschaffen and Kayles (R and K) may be used as well as tables showing weighted values and actual movement data for each sensor.
The vigilance algorithm(s) may determine a vigilance probability factor (0-100%) as a function of weighted movement data values.
Upon detecting a vigilance probability factor which is below an acceptable threshold, the system may be arranged to intervene in the control of the vehicle or to alert the driver of the vehicle and/or other vehicles. Vehicle control intervention may include restriction of speed, controlled application of brakes, cutting-off fuel and/or disabling the accelerator pedal. Driver alerting intervention may include use of sprays designed to stimulate the driver, vibrating the steering wheel, seat belt or floor area in the vicinity of the driver, an audible alarm and/or use of bright cabin lights. The driver can also be alerted by winding down the driver window and/or other effective alerting methods as may be applicable to each individual driver. Drivers of other vehicles may also be alerted by means of flashing hazard lights and/or sounding of a siren. Vehicle control intervention may be integrated with and form part of a vehicle control system or it may be interfaced to an existing vehicle control system. Vehicle control intervention may be interfaced with GSM or other communication systems to provide early warning indication that a driver or operator of equipment is in a stressed, fatigued or other undesirable condition that may be detected.
To assist differentiating normal and acceptable driver vigilance from fatigued or inappropriate driver conditions, calibration of the various sensor and transducer outputs is possible. Calibration can set the system""s detection parameters in accordance with varying driver movement and other driver signals. Calibration is beneficial because driver sensor and signal outputs will vary with different drivers. Background noise will also vary with different vehicles. The need for calibration may be proportional to the critical nature of the driving or dependent on the level of accuracy required for fatigue monitoring and detection.
The need for calibration may to some extent be removed by utilizing artificial intelligence to distinguish baseline conditions for a drivers normal wakeful state to facilitate subsequent analysis and determining when a driver""s state indicates fatigue or lapse of vigilance. Artificial intelligence may be embodied in one or more automated systems including one or more mathematical algorithms. Artificial intelligence includes the systems ability to self-learn or teach itself conditions associated with the driver which constitute normal or alert driving as distinct from conditions which constitute abnormal or fatigued driving.
Artificial intelligence may allow the driver of a specific vehicle to select a mode of operation during which the driver""s movements during normal or wakeful driving are monitored and diagnosed in order to determine typical thresholds and correlations between various sensors, for the purpose of determining true fatigue states of the driver as distinct from alert states of the driver. Artificial intelligence may also facilitate adaptation of the vigilance algorithm(s), to the specific vehicle""s background noise characteristics.
Artificial intelligence may include different response patterns for correlating movement data from the various sensors for distinguishing valid driver movements from environmental vibrations and noise. These may be classified and described by, for example, a look up table that records expected patterns or combinations of signals for different cases of environmental noise as distinct from driver generated signals. For example, if the driver moves his hand, signals from sensors in the steering wheel and arm sections of the seat may correlate according to a specific pattern. Alternatively, if the vehicle undergoes a severe or even subtle vibration due to road or engine effects, a broader range of sensors may be similarly affected and this may be manifested as amplitudes which follow predetermined correlation patterns. Signals from the sensors may increase in strength or amplitude according to the proximity of the source of the sound or vibrations. Where the source of the vibration is localized, this may manifest itself as a pattern of similar waveforms across the various sensors which reduce progressively in amplitude as the sensors distance from the source increases. For example, if the source of the vibration is road noise, the floor sensors may register maximum amplitude whereas the steering wheel sensors which are furthest from the road noise may register minimum amplitude.
The phase relationship of vibrations from various sources may also provide some guide as to the likely source of the vibrations. For example, if the vibrations emanate from the driver""s movement then it is more likely that several signals with similar phase may be detected. On the other hand, if the signals have varying phase relationships, then it is more likely that the source of the vibrations giving rise to these signals is random as may be expected if the vibrations emanate from the vehicle environment.
Similar phase signals arising from driver movements may be distinguished from similar phase signals arising from artefacts or the vehicle environment by relating the environmental noise to sensors located near sources of expected noise in the vehicles, eg. engine noise, wheel noise, and other vibrations and noise. This may be detected by carefully locating microphones and vibration sensors in the vehicle.
Cancellation of environmental noise can be assisted by monitoring signals from the microphones and sensors with a view to applying the most effective signal cancellation techniques in order to reduce as much as possible the artefact or noise effects or unwanted signals within the vehicle environment.
One example of the application of noise cancellation techniques includes detection of the various road bumps and ignoring the effect of these bumps on the data being analysed from the various vehicle sensors of interest.
Another example of the application of noise cancellation techniques includes detection of various engine noises and application of a signal of opposite phase to the motor noise in order to cancel the artefact. One example of phase cancellation techniques which may be adopted is disclosed in PCT application AU97/00278, the disclosure of which is incorporated herein by cross-reference.
Other examples of noise cancellation include filtering wherein highpass, lowpass and notch filters may be used to assist artefact removal. Artificial intelligence may learn to ignore periodic signals from sensors in the vehicle as these are likely to arise from mechanical rotations within the vehicle, thus improving the separation of artefact signals from signals of interest, such as signals which indicate true driver movement.
Artificial intelligence may also learn to recognize changes in the driver""s state which reflect changes in driver vigilance or wakefulness. Points of calculation and analysis of sensor data for the purpose of comparison and correlation with previously monitored data may include:
spectral analysis of signals with a range of consecutive time periods;
xc2xd period time amplitude analysis of signals and other techniques used in conventional sleep analysis as disclosed in AU Patent 632932;
calculation of the number of movements per consecutive periods of time, wherein the consecutive periods of time may typically be, 1 second or {fraction (1/2 )} second;
calculation of average signal levels during periods of, say, 20 or 30 seconds;
calculation of total xe2x80x9carea under the curvexe2x80x9d or integration of sensor signals for a period of, say, 20 or 30 seconds;
correlation and relationship between various combinations of input sensor channels;
ECG heart rate and respiration signals, the latter signals providing an indication of the drivers wakeful state, as heart-rate and respiration signals during the sleep state are well documented in a number of medical journals.
Artificial intelligence may be applied in conjunction with pressure sensors in vehicle seats and/or seat belts to control air bag deployment. In this way air bag deployment may be restricted for children or validated with different crash conditions for children and adults. For example, if a child is not detected as being thrust forward by means of pressure data received from seat/seat belt sensors, deployment of air bags and possible air bag injury to the child may be avoided.
Deployment of air bags may generally be validated more intelligently by analysing data relating to passenger or driver posture, movement, thrust, body movement, unique driver or passenger xe2x80x98yawxe2x80x99 etc.
The system may include means for testing a driver""s response times. xe2x80x9cSuch tests may, if carried out at regular intervals, pre-empt serious driving conditions as can be brought about by driver fatigue or a lapse in vigilance. The testing means may be adapted to provide a simple method for prompting the driver and for testing the driver""s response time. The response test may, for example, request the driver to respond to a series of prompts. These prompts may include requesting the driver to squeeze left or right hand sections of the steering wheel or squeeze with both left and right hands at the same time in response to a prompt. The means for prompting the driver may include, for example, LEDs located on the dash of the vehicle or other position that the driver is visually aware of. A left LED blinking may for example, prompt the driver to squeeze the left hand on the steering wheel. A right LED blinking may prompt the driver to squeeze the right hand on the steering wheel. The centre LED blinking may prompt the driver to squeeze both hands on the steering wheel. Alternatively two LEDs could be used in the above example, except that both LEDs blinking may prompt the driver to squeeze with both hands.
The drivers response or level of alertness may be detected by measuring the response time of the driver, where the response time is measured as the time between illumination of an LED and a correct response with the hand or hands. In a case where an inappropriate response time is detected (potentially signalling driver fatigue or onset of driver fatigue) the system can verify the results and alert the driver. The system may also determine the accuracy of the driver""s responses to ascertain the status of the driver""s vigilance.
A further example of means for testing the driver""s response may include means for flashing random numbers on the windscreen. The driver may be prompted to respond by squeezing the steering wheel a number of times as determined by the number flashed. The numbers may be flashed on the screen at different locations relative to the steering wheel with the position of the hands on the wheel responding to the side of the screen where the flashes were detected. This type of test should be conducted only when the driver is not turning, changing gear, braking or performing other critical driving functions.
It is desirable to ensure that the driver response tests are not anticipated by the driver to more accurately detect the driver""s state of vigilance. It is of course also important that the selected method of testing driver response, does not in any way distract the driver or contribute to the driver""s lapse in concentration.
The system may be built into a vehicle sun visor as a visual touch screen display allowing a comprehensive visualisation of a drivers activity. The touch screen may include a color display for displaying movement/pressure outputs associated with each sensor. A display of the status of a plurality of sensors may provide a visual indication of a relaxed versus an active driver state.
According to one aspect of the present invention, there is provided apparatus for determining a vigilance state of a subject such as a driver of a vehicle or the like, said apparatus including:
means for monitoring one or more physiological variables associated with said subject;
means for deriving from said one or more variables data representing physiological states of said subject corresponding to the or each variable; and
means for determining from said data when the vigilance state of said subject is below a predetermined threshold.
According to a further aspect of the present invention, there is provided a method for determining a vigilance state of a subject such as a driver of a vehicle or the like, said method including the steps of:
monitoring one or more physiological variables associated with said subject;
deriving from said one or more physiological variables data representing physiological states of said subject corresponding to the or each variable; and
determining from said data when the vigilance state of said subject is below a predetermined threshold.